A Unified Multiple-Target Positioning Framework for Intelligent Connected Vehicles
1
State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
2
Department of Electrical Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 1967; https://doi.org/10.3390/s19091967
Received: 11 April 2019
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Revised: 22 April 2019
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Accepted: 24 April 2019
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Published: 26 April 2019
(This article belongs to the Collection Multi-Sensor Information Fusion)
Future intelligent transport systems depend on the accurate positioning of multiple targets in the road scene, including vehicles and all other moving or static elements. The existing self-positioning capability of individual vehicles remains insufficient. Also, bottlenecks in developing on-board perception systems stymie further improvements in the precision and integrity of positioning targets. Vehicle-to-everything (V2X) communication, which is fast becoming a standard component of intelligent and connected vehicles, renders new sources of information such as dynamically updated high-definition (HD) maps accessible. In this paper, we propose a unified theoretical framework for multiple-target positioning by fusing multi-source heterogeneous information from the on-board sensors and V2X technology of vehicles. Numerical and theoretical studies are conducted to evaluate the performance of the framework proposed. With a low-cost global navigation satellite system (GNSS) coupled with an initial navigation system (INS), on-board sensors, and a normally equipped HD map, the precision of multiple-target positioning attained can meet the requirements of high-level automated vehicles. Meanwhile, the integrity of target sensing is significantly improved by the sharing of sensor information and exploitation of map data. Furthermore, our framework is more adaptable to traffic scenarios when compared with state-of-the-art techniques.
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Keywords:
vehicular localization; target positioning; high-definition map; vehicle-to-everything; intelligent and connected vehicles; intelligent transport system
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MDPI and ACS Style
Xiao, Z.; Yang, D.; Wen, F.; Jiang, K. A Unified Multiple-Target Positioning Framework for Intelligent Connected Vehicles. Sensors 2019, 19, 1967. https://doi.org/10.3390/s19091967
AMA Style
Xiao Z, Yang D, Wen F, Jiang K. A Unified Multiple-Target Positioning Framework for Intelligent Connected Vehicles. Sensors. 2019; 19(9):1967. https://doi.org/10.3390/s19091967
Chicago/Turabian StyleXiao, Zhongyang, Diange Yang, Fuxi Wen, and Kun Jiang. 2019. "A Unified Multiple-Target Positioning Framework for Intelligent Connected Vehicles" Sensors 19, no. 9: 1967. https://doi.org/10.3390/s19091967
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